Considering that previous literature has mainly focused on the impact of the digital economy (DE) on environmental degradation, ignoring the role of natural resources, this study uses two key factors (natural resource rent and anticorruption regulation) as threshold variables to reveal the effect of natural resources on the association between DE and carbon dioxide (CO₂) emissions. In doing so, the study covers 97 countries, uses annual data between 2003 and 2019, and applies a panel threshold model. The outcomes present that the influence of the DE on CO₂ emissions has a single-threshold effect (i.e., there is an inverted U-shaped link between the DE and CO₂ emissions) when natural resource rent is the threshold variable. Specifically, the DE significantly increases CO₂ emissions when the natural resource rent is at a low-to-medium level, but the DE suppresses CO₂ emissions growth when natural resource rent exceeds the threshold. Moreover, the DE drives overall CO₂ emissions growth when anticorruption regulation is the threshold variable and there are double thresholds for its impact on CO₂ emissions. Specifically, a rise in anticorruption regulation initially exacerbates the contribution of DE impact on CO₂ emissions and then weakens it over time. Based on the results, the study proposes various implications, such as formulating a DE development strategy, considering natural resources in the development of the DE, and strengthening anti-corruption efforts in the field of environmental protection.

Stimulating renewable energy consumption is a major focus of the Sustainable Development Goals in combating climate change and global warming. The International Energy Agency estimates that renewable energy consumption should be doubled to achieve the COP21 targets. In this context, the question is whether renewable energy types promote the improvement of ecological quality and economic growth. Most studies have investigated the influence of renewable energy on ecological pollution using carbon dioxide emissions or ecological footprint indicators, which only represent the pollution caused by human consumption patterns, and these indicators neglect the supply side. Motivated by this point, this study uses the LCF (Load Capacity Factor) as an environmental indicator and examines the causality relationship among different types of renewable energy, income, and environmental quality in the USA, while also incorporating employment and capital stock into the analysis. Through using the Fourier causality test with the wavelet-decomposed series, the study explores for the validity of the renewable energy-based growth hypothesis and answers to the question of whether there is a causal effect of renewable energy types on environmental quality. The results demonstrate that there is a bidirectional causality between total renewable energy, wood, biomass, and economic growth as well as between these renewable energy types and the LCF.

Renewable energy, energy efficiency, and nuclear energy research and development (RER, EER, and NER) budgets are immensely important to fulfill sustainable development goals 7, 9, and 13, by accelerating energy innovation, energy transition, and climate control. The literature on the drivers of the load capacity factor (LCF), a recently developed ecological quality measure, is mounting; however, the roles of energy investments in the LCF are largely unknown. Accordingly, this study assesses the impacts of RER, EER, NER, and financial globalization (FIG) on the LCF using data from 1974 to 2018 for Germany. Advanced and reliable time series tests (Augmented ARDL, DOLS, and Fourier causality) are adopted to analyze cointegration, long-run impacts, and causal connections. The outcomes unveil that both green energy and energy efficiency R&D promote the LCF by enhancing ecological quality. However, the positive impact of NER on the LCF is found to be weaker than the impacts of RER and EER. FIG curbs ecological degradation by expanding the LCF. Additionally, the U-shaped connection between economic growth (ECG) and the LCF confirms the load capacity curve. Therefore, policymakers should focus on RER and EER to preserve the environment and promote sustainable growth.

All economies are concerned about rising carbon emissions, which contribute to environmental degradation. The current paper formulates a novel framework to scrutinize the impacts of shocks in economic complexity, FDI, environmental technology, and renewable energy on carbon emission in the leading clean energy investment countries, spanning the period from 1995 to 2020. In spite of the constraint for better environmental defence and the realization of the Sustainable Development Goals (SDGs), this paper introduces an empirical approach utilizing the Panel NARDL methodology to investigate the asymmetrical connections between carbon emissions and relevant exogenous factors. Furthermore, we utilize additional techniques, namely AMG and CCEMG, to enhance the robustness of our findings. Our empirical findings reveal that positive shocks in economic complexity, FDI, environmental technology, and renewable energy reduce carbon emissions while negative shocks may result to elevated pollution levels in the long-run. However, adverse shocks in economic complexity and FDI cause increased pollution in the long run. Likewise, the short-run coefficient signs are also similar to the long-run coefficient signs but different in significance level and magnitude. This has paved the way for a well-designed policy for leading clean-energy investment countries should focus on structural change, FDI, technology and renewable energy consumption.

A sharp increase in economic and human development has multiplied the carbon intensity due to which there is a significant need of effective strategies in order to curb carbon emissions. Thus, the present study aims to examine the effective of green finance, eco-innovation, renewable energy output (REO), renewable energy consumption (REC), and carbon taxes on carbon dioxide (CO₂) emissions in BRICS countries in the time of 2001–2020. Cross-sectional autoregressive distributed lag (CS ARDL) is used to test the connection among the variables. Empirical estimations of CS-ARDL approach validates the effectiveness of green finance, eco-innovation, REO, REC, carbon taxes, and industrialization as the relationship of these factors with carbon emissions is negative in nature in BRICS economies. Based on the evidences, the study recommends the formulation of environmentally friendly practices and advancement in green finances to mitigate carbon emissions.

There is growing attention from governments and regulators towards crucial matters such as climate change and global warming, resulting in a pressing need to investigate the factors that make it possible for businesses to engage in green finance (GF). The externality of environmental pollution prioritizes the need of green innovation (GI) in public management. GF distributes financial resources to the research and development (R&D) of clean energy and environmentally friendly goods and processes; it is complementary to the GI process for environmental protection. GF policies help to alleviate the impacts of financial constraints and GI impaired industries involving new products, processes, services and the global market. To better understand how GF and GI have functioned as a catalyst for circular economy practices, this paper seeks to present a historical and contemporary overview of these concepts. The research is thoroughly dissected by a systematic literature evaluation of articles from 2016 to 2023 that appear in peer-reviewed journals and are indexed in the SCOPUS database. To attain supply chain circularity, this article encompasses four major research themes concerning the adoption of GF and green technologies. The research also includes a network analysis of shortlisted articles to examine the overall citation trends. It is shown that several institutional theories are associated with the investigated area. As a final step, a framework is provided to illustrate how GF and GIs might be used to achieve supply chain circularity. The research findings provide a novel concept related to GF within the context of GI which are significant for environmentalists, policymakers, green investors, and researchers. Through its findings, the study provides a conceptual framework that promotes sustainable strategies to effectively balance financial considerations and environmental innovation. It helps to leverage the potential of green research and practice to create value for businesses and to benefit society at large. The analysis provides an unexplored and significant contribution to current literature in terms of delivering evidence of the past and present approaches to GF and GI in a circular economy. The results of this study will attract the attention of policymakers and stakeholders to develop and combine the two concepts in research and practice to attain environmental balance in the circular economy and to promote long term sustainability.

Natural resources represent the base of our living and the entire economic activity. Their depletion is a major challenge for the economic development of both developed and developing economies. Their efficient use is an indispensable requirement and must be the aim of the public policies designed by the authorities worldwide. In this research, we have investigated the impact of the natural resources rent on the economic growth in some major wealthy economies of the world (P5 + 1 countries namely: US, UK, France, China, Russia, and Germany). We have applied a quantile-on-quantile regression to analyse this impact on different quantiles and a cross-sectional autoregressive distributed lag (CS-ARDL) approach for the panel of these six countries. The Dumitrescu-Hurlin panel causality test was also used to check the causality between natural resource rents and economic growth in these countries. Results show a negative relationship between natural resources rent and economic growth for the panel but a different impact on quantiles in each country. Only for China and the US, a positive effect can be noticed for both lower and higher quantiles of natural resources and economic growth. The Dumitrescu-Hurlin causality test shows that natural resources can predict economic growth only in China, the U.S., and the panel. In contrast, no causality was found for the other four countries included in the panel. We suggest that nations invest in wind and solar projects, use biofuels and nuclear energy, introduce a temporary profit tax to protect consumers from escalating energy prices, and increase energy efficiency in buildings and industry. Businesses would benefit from a regulatory framework that is uniform and exhaustive, as well as easier to traverse and more receptive to innovation and creativity. Public-private partnership investments in innovation, innovation incentives, and environmental sector opportunities may foster long-term economic growth.

Low carbon productivity has been identified as a key direction for China’s future development. As an important driving force for economic growth, the question of whether digital finance that is reliant on digital technology can support the development of a low-carbon urban economy remains unresolved. Based on the carbon productivity measured by panel data from 201 cities for the period 2011–2020, this study applies the spatial Dubin model and threshold regression model to explore the impact of digital finance on carbon productivity, yielding the following key conclusions. First, the spatial distribution heterogeneity of carbon productivity in China’s eastern region is higher than that in the western region, and both productivity and digital finance are characterized by high (low)–high (low) dotted spatial agglomeration. Second, digital finance can significantly improve carbon productivity via two transmission channels: the human capital and marketization effects. At the same time, digital finance exerts a spatial spillover effect on carbon productivity, and rising local digital finance levels will increase carbon productivity in neighboring areas. Heterogeneity analysis indicates that the spillover effect of digital finance in urban agglomerations and eastern regions is more significant. Third, fixed-asset investment has a positive nonlinear moderating effect on digital finance, thus improving carbon productivity. When the per capita investment in fixed assets does not exceed 682.73 yuan, digital finance exerts only a limit pulling effect on carbon productivity; when it is higher than this value, the pulling effect is intensified.

This research explores the dynamic relationships between ecological footprint, economic performance, financial development, energy usage, and foreign direct investment (FDI) in South Asian economies utilizing the panel data from 1971 to 2018. In panel data analysis, conventional methods generally ignore the issues of cross-sectional dependency and the heterogenous nature of cross-sectional units. The other concern with the existing research is that most of the studies ignore the significance of ecological footprint while evaluating financial development and FDI as sources of environmental changes. The long-term relationship among the indicators is tested utilizing the Westerlund cointegration test. The findings support both environmental Kuznets curve and pollution haven hypotheses for South Asian economies. Besides, the empirical findings suggest that financial development increases environmental conservation while energy usage substantially disrupts the environment of the selected south Asian nations. Additionally, the heterogeneous causality analysis reveals the causal relationships between the variables. Thus, overall results recommend that the South Asian economies need to boost economic growth without compromising the environment, decrease fossil fuel usage, enhance financial sector growth and incentivize environmentally friendly FDI to conserve the environment in the region.

The challenge of achieving sustainable economic development with a secure environmental system is a global challenge faced by both developed and developing countries. Energy Efficiency (EE) is crucial in achieving sustainable economic growth while reducing ecological impacts. This research utilizes the Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist-Luenberger Index (MLI) method to evaluate EE and productivity changes from 1995 to 2020 across G20 countries. The study uses four different input–output bundles to gauge the impact of renewable and non-renewable energy consumption and carbon emissions on EE and productivity changes. The study results show that including renewable energy consumption improves the average EE from 0.783 to 0.8578, but energy productivity declines from 1.0064 to 0.9988. Incorporating bad output (carbon emissions) in the estimation process enhances renewable EE and productivity change, resulting in an average EE of 0.6678 and MLI of 1.0044. Technological change is identified as the primary determinant of energy productivity growth in scenarios 1 and 2, while technical efficiency determines energy productivity change in scenarios 3 and 4. The Kruskal-Wallis test reveals a significant statistical difference between the mean EE and MLI scores of G20 countries.

Green power conversion is the shift away from traditional fuels towards clean energy sources such as nuclear power plants, hydroelectric dams, wind farms, and solar panels. This research examines the impact of clean energy demand and green financing on reducing carbon emissions in 29 economies in Europe and Asia from 2007 to 2020. The study used a two-step differenced GMM estimator for the available data set spanning 2007 to 2020. The study found that rising demand for nuclear power helps to achieve a carbon-neutral agenda, but insufficient funding for renewable energy leads to higher carbon emissions. The research suggests increasing investment in nuclear energy and green financing can improve regional environmental quality. The study found a causal link between fuel imports, nuclear power and regional growth. It also determined that fuel imports, chemical use, green financing and the need for nuclear energy will likely impact regional environmental quality. The research recommends allocating more resources toward innovation to boost energy efficiency and expanding investment in renewable and nuclear energy production industries via green finance. The study also highlights the need to encourage the development of renewable energy sources to cut carbon emissions and establish a sustainable society.

Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks. However, traditional methods usually emphasize on larger regions of landsliding and rely on relatively static environmental conditions, which exposes the hysteresis of landslide susceptibility assessment in refined-scale and temporal dynamic changes. This study presents an improved landslide susceptibility assessment approach by integrating machine learning models based on random forest (RF), logical regression (LR), and gradient boosting decision tree (GBDT) with interferometric synthetic aperture radar (InSAR) technology and comparing them to their respective original models. The results demonstrated that the combined approach improves prediction accuracy and reduces the false negative and false positive errors. The LR-InSAR model showed the best performance in dynamic landslide susceptibility assessment at both regional and smaller scale, particularly when identifying areas of high and very high susceptibility. Modeling results were verified using data from field investigations including unmanned aerial vehicle (UAV) flights. This study is of great significance to accurately assess dynamic landslide susceptibility and to help reduce and prevent landslide risk.

New energy development is essential to achieving carbon peaks and neutrality and promoting green technological innovation. Identifying the causal relationship between new energy demonstration city construction and green technological innovation is crucial for the expansion and promotion of new energy demonstration cities. In this study, we take the construction of new energy demonstration cities as a quasi-natural experiment, study their impact on green technological innovation using difference-in-difference (DID), and conduct a robustness test using DID after propensity score matching (PSM-DID). The research results indicate the following: First, energy structure optimization can significantly improve the level of urban green technological innovation (this result was shown to be valid using PSM–DID and other tests involving the effects of placebo and instrumental variables). Second, new energy demonstration city construction mainly improves the level of urban green technological innovation through technology research and development, the improvement of the industrial innovation environment, and the promotion of environmental performance. Third, the impact of energy structure optimization on green technological innovation has regional, financial, and economic development heterogeneity. Finally, new energy demonstration city policy affects the flow of capital, labor, technology, and other production factors to pilot areas according to new energy demonstration city policy, forming a “siphon effect”. The carbon reduction effect of new energy demonstration city construction is greater than its pollution reduction effect. Given the results of the study, policy recommendations to promote the expansion of new energy demonstration cities are proposed.

The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price accurately. The main contribution is focused on eastern region of Saudi Arabia, a relatively hottest geographical area full of energy resources but with different electricity consumption patterns. The relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous studies. In the eastern region, electricity price has negative association with electricity consumption. While comparing traditional and machine learning, it is found that machine learning techniques offer better predictability. Amongst the machine learning techniques, the support vector machine has the lowest errors in forecasting the electricity price. Additionally, the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity consumption. The findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.

This paper investigates the effect of the circular economy on CO₂ emissions growth by considering the role of energy transition, climate policy stringency, industrialization, and supply chain pressure from 1997 to 2020 using panel quantile Autoregressive Distributed Lags (QARDL) and the panel PMG. We employ cointegration association in the long run among the variables, and the results of the two models confirm this. Findings reveal that circular economy and climate policy stringency significantly negatively impact carbon emissions. On the other hand, the energy transition, industrialization, and supply chain pressures are crucial to determining CO₂ emissions in the short and long run. The finding further explores that municipal waste generation recycling is considerable at the mean and upper 90th quantiles than the lower quantile. Therefore, the empirical results of the current study provide acumens for policymakers of advanced economies and emerging markets to maintain the balance among circular economy, energy transition, environmental policy stringency, and supply chain pressure for reducing CO₂ emissions without halting economic growth and sustainable development. Furthermore, practical implications are reported through the lens of carbon neutrality and structural changes.

Aggregate demand or supply at equilibrium is commonly used as a representative of the macroeconomic activity of an economy whereby aggregate demand denotes the behaviour of individuals and households. However, aggregate demand can also directly affect environmental deterioration via changes in aggregate production. This study tried to explore this relationship, known as the demand-based Environmental Kuznets Curve (Demand EKC) and the role of different knowledge economy indicators. Knowledge economy indicators are proposed to influence consumption patterns, altering the demand EKC that empirical studies have understudied. For this purpose, secondary data for 147 countries were collected from 2008 to 2018, also classified as development-wise. This study found that aggregate demand significantly affects carbon emissions. The long-run results are estimated using the Fully Modified Ordinary Least Square method. Controlling factors like renewable energy consumption, population density, and financial development significantly affect carbon emissions in sample countries. This study has incorporated four pillars of a knowledge-based economy and the results showed that these indicators helped reduce consumption-related CO₂ emissions.

As the extraction and usage of natural resources continue to be a double-edged sword – supporting economic growth but deteriorating the environment- we study the impact of natural resource mining on sustainable economic development in the largest (PPP) economy – China. We use province-level data from 2001 to 2020 and employ econometric panel techniques, such as fixed effects, two-stage least squares, and a battery of robustness tests. We further explore the potential effects of education and green innovation in mitigating/exacerbating the role of natural resources in the Chinese provincial economy. The results show that: (1) Natural resource mining hurts sustainable development, verifying the “resource curse” effect. (2) Green innovation and education restrain the negative impact of resource mining on sustainable development, turning the curse into a blessing. (3) A regional heterogeneity is observed in the impact of resource mining on sustainable development, showing more significant effects in the Western and low-urbanized regions. (4) Green innovation and education can assuage the curse effect of natural resources into gospel effect. Policy implications and recommendations are proposed in light of the findings to promote sustainable economic development in China.

In this study, the relationships between five renewable energy sub-sectors markets and the geopolitical risk (GPR) and economic uncertainty indices (EUI) were examined using daily data from March 30, 2012, to April 1, 2022. Convergent cross mapping results show that the renewable energy indices have definite relationships with the GPR and EUI. The renewable energy indices show differences in response directions, speed and trends for a standard information difference impulse from the GPR and the EUI. A positive dynamic conditional correlation between renewable energies and EUI was observed in the first and second waves of the COVID-19 outbreak. In contrast, there was a relatively decreased effect for two risk indices during the Russia–Ukraine conflict of February–March 2022. Our results show that renewable energy may act as a time-varying hedge against economic uncertainty and GPR owing to its safe-haven properties at various scales. Moreover, building more secure and reliable renewable energy systems can help countries to increase their energy independence, which protects them against the risks of political and economic uncertainty.

Copper is one of the most important minerals that has extensive use in environment-friendly technologies and renewable energy generation. The global urgency for environmental and ecological conservation through renewable energy transition has considerably enhanced the importance of copper and articles thereof. Chile is a major producer of copper. It contributes more than one-third to global supply. Therefore, this study explores the export flow of Chilean copper in response to increasing demand side conditions in major 24 trading partners from 2002 to 2020. This objective is realized by constructing an augmented model for import demand that incorporates bilateral real exchange rate along with real GDP, environmental innovation, and renewable energy transition in major import markets. The estimated results of panel quantiles via moments techniques reveal a significant positive impact with increasing coefficients at higher quantiles, while environmental innovation and renewable energy transition in trading partners show significant positive impact with decreasing values of coefficients at higher quantiles. The findings urge Chile to enhance production capacity of copper and other critical mineral and improve participation in global value chain to meet sharply increasing copper demand from environmental innovation and renewable energy transition.

Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures. The prevailing models mostly adopt the form of empirical functions, employing mathematical regression techniques to represent experimental data. As an alternative approach, this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength. Five metaheuristic optimization algorithms, including the chameleon swarm algorithm (CSA), slime mold algorithm, transient search optimization algorithm, equilibrium optimizer and social network search algorithm, were employed to enhance the performance of the multilayered perception (MLP) model. Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models, employing statistical indicators such as root mean square error (RMSE), correlation coefficient (R²), mean absolute error (MAE), and variance accounted for (VAF) to evaluate the performance of each model. The sensitivity analysis of parameters that impact joints shear strength was conducted. Finally, the feasibility and limitations of this study were discussed. The results revealed that, in comparison to other models, the CSA-MLP model exhibited the most appropriate performance in terms of R² (0.88), RMSE (0.19), MAE (0.15), and VAF (90.32%) values. The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength. This paper presented an efficacious attempt toward swift prediction of joints shear strength, thus avoiding the need for costly in-site and laboratory tests.

The literature on landslide susceptibility is rich with examples that span a wide range of topics. However, the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored. This statement holds true, particularly in the context of landslide risk, where few scientific contributions investigate risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). For the analyses, the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit, resulting in a total of 236,997 units. This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, as we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model, assuming vulnerability = 1. Overall, this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.

The energy transition challenges faced by modern civilization have significantly enhanced the demand for critical metals like lithium resulting in improved methods to explore, extract, and utilize these metals. In this comprehensive review, we discuss the different types of lithium resources, factors, and mechanisms controlling lithium enrichment in various geological settings including terrestrial and marine environments. Diverse exploration strategies including geological, geophysical, mineralogical, geochemical, and remote sensing techniques including drone-based techniques for lithium exploration studies in different terranes are summarized. An overview of the mining techniques, including beneficiation and extraction, and their principles, mechanisms, operations, and comparison of the various approaches and compatibility with different types of lithium deposits for obtaining maximum yield are evaluated. Lithium isotopic studies are useful in understanding geological processes such as past weathering events and riverine input into the oceans, as well as in understanding the source of lithium in diverse types of deposits. We also highlight the recent developments in other areas such as recycling, environmental impact, and state-of-the-art analytical techniques for determining lithium in different lithium ore deposits and other geological materials. Our overview provides the latest developments and insights in the various sectors related to lithium and prompt further developments to meet the growing demand for this valuable metal as the world transforms to clean energy.

The continuous rise in global environmental challenges has led to urgency toward establishing a secure framework to achieve sustainable development goals. This study establishes a novel theoretical framework to analyze the role of energy prices, energy consumption, gold prices and economic growth on environmental degradation in newly industrialized economies. To realize sustainable development goals and foster environmental defence, this study utilizes CS-ARDL as the main econometric approach to investigate the asymmetric association between environmental degradation and relevant factors. We also use AMG, CS-DL, Driscoll-Kray and FGLS to enhance the robustness of our findings. Our econometric approach reveals that energy resource prices and renewable energy consumption reduce environmental degradation, while gold prices and fossil energy consumption elevate environmental pollutants. We also confirm the existence of the EKC hypothesis. The findings of our extensive analysis paved the way for a well-designed environmental policy for NIC economies should focus on renewable energy consumption, green investments, and structural changes.

Landslide inventory is an indispensable output variable of landslide susceptibility prediction (LSP) modelling. However, the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored. Adopting Xunwu County, China, as an example, the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions, after which different landslide inventory sample missing conditions are simulated by random sampling. It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%, 20%, 30%, 40% and 50%, as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation. Then, five machine learning models, namely, Random Forest (RF), and Support Vector Machine (SVM), are used to perform LSP. Finally, the LSP results are evaluated to analyze the LSP uncertainties under various conditions. In addition, this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions. Results show that (1) randomly missing landslide inventory samples at certain proportions (10%–50%) may affect the LSP results for local areas. (2) Aggregation of missing landslide inventory samples may cause significant biases in LSP, particularly in areas where samples are missing. (3) When 50% of landslide samples are missing (either randomly or aggregated), the changes in the decision basis of the RF model are mainly manifested in two aspects: first, the importance ranking of environmental factors slightly differs; second, in regard to LSP modelling in the same test grid unit, the weights of individual model factors may drastically vary.

This study aims to demystify the role of green energy and green technology in establishing the nexus between behavioural intentions of tourists, technologies, and digital payments by using Perceived value (PV), Compatibility (CO), Perceived Enjoyment (PE), and Social Influence (SI) as a predictor variables, Trust (TR) and Satisfaction (SA) as a mediating variables and Behavioural Intentions (BI) as an outcome Variable. For the empirical estimation, we employ smart PLS-SEM, TAM (Technology Acceptance Model) and SPSS and Tested the LCC hypothesis. Key findings suggest that green energy and perceived value have the highest positive impact on tourists' trust towards digital payments followed by compatibility, social influence and perceived enjoyment. Similarly, tourists’ satisfaction and green technology is one of the important determinants of choosing any digital mode of payment, is mostly influence by perceived value, perceived enjoyment, compatibility and social influence. Moreover, if we choose between trust and satisfaction, trust plays a significant role in exploring the behavioural intentions of tourists about green energy and green technology followed by tourists’ satisfaction. In addition, Tourists’ trust and satisfaction are highly correlated and influence each other. The study offers novel policy implications in terms of use of green technology and green energy in enhancing trust and satisfaction of tourists in order to deeper understanding of different dimensions of digital payments and M−wallets, and allowing them to explore the long-term value inherent of digital payments and M−wallets.

Covered by erodible loess and affected by significant seasonal climate variations, chemical weathering in the Chinese Loess Plateau (abbreviated as CLP) has important effects on the hydrochemistry of the Yellow River and the global carbon cycle. However, chemical weathering processes in the CLP are still unclear. Based on 296 river water samples in the CLP in the different seasons, hydrochemistry, weathering processes, and their controlling factors were revealed. River waters in the CLP exhibited slightly alkalinity (pH = 8.4 ± 0.5) with much high total dissolved solids (TDS) values (691 ± 813 mg/L). The water types of river water in the CLP were primarily SO₄²⁻ − Cl⁻ − Na⁺, HCO₃⁻ − Ca²⁺ − Mg²⁺, and SO₄²⁻ − Cl⁻ − Ca²⁺ − Mg²⁺. According to the forward model, evaporite dissolution has the largest contribution (55.1% ± 0.2%) to riverine solutes in the CLP, then followed by carbonate weathering (35.6% ± 0.2%) and silicate weathering (6.5% ± 0.1%). For spatio-temporal variations, the contribution of evaporite dissolution in the CLP decreased from northwest to southeast with higher proportion in the dry season, carbonate weathering increased from northwest to southeast with a higher proportion in the wet season, and silicate weathering showed minor spatio-temporal variations. Ca²⁺ and Mg²⁺ were affected by carbonate precipitation and/or incongruent calcite dissolution, and about 50% of samples exhibited cation exchange reactions. The physical erosion rate in the CLP, which was 372 ± 293 t·km⁻²·yr⁻¹, varied greatly and was greater than those of other worldwide rivers. Chemical weathering rates in the CLP showed an increasing trend southward. During the wet season, high runoff led to the release of evaporite and carbonate from loess, while the interfacial reaction kinetic limited the increase of the silicate weathering rates. The CO₂ consumption budget by carbonate weathering (6.1 × 10¹⁰ mol/yr) and silicate weathering (1.6 × 10¹⁰ mol/yr) in the CLP accounted for 0.29% and 0.08% of the global carbon cycle, respectively. Meanwhile, the weathering proportion by sulfuric acids was relatively high with a CO₂ release flux of 6.5 × 10⁹ mol/yr. By compiling the data, we propose that the interfacial reaction kinetic and runoff control CO₂ consumption rate by silicate and carbonate weathering, respectively. These results contribute to the understanding of modern weathering processes of loess in the CLP, thus helping to deduce the environmental and climatic evolution of the basin.

Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies, because current methods are designed using limited number of data and cannot handle large datasets. In geoscience, tectonic discrimination illustrates this issue, using geochemical analyses to define tectonic settings when most of the geological record is missing, which is the case for most of the older portion of the Earth’s crust. Basalts are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings, and their chemical compositions can be an effective way to understand tectonics-related mantle processes. However, the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique solutions. Also, weathering, erosion, and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic records. To address those limitations, supervised machine learning techniques (a part of artificial intelligence) are being utilized more often as a tool to analyze multidimensional datasets and statistically process data to tackle big data challenges. This contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine learning. Deep learning, especially Convolutional Neural Network (CNN) is the most accurate approach. However, it requires a large dataset and considerable processing time, and the gain of accuracy can be at the expense of interpretability. Therefore, this study designed guidelines for data pre-processing, tectonic setting classification and objectively evaluating the model performance. We also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research, aimed at closing the divide between earth scientists and data scientists.

The representation of spatial variation of soil properties in the form of random fields permits advanced probabilistic assessment of slope stability. In many studies, the safety margin of the system is typically characterized by the term “probability of failure (P_failure)”. As the intensity and spatial distribution of soil properties vary in different random field realizations, the failure mechanism and deformation field of a slope can vary as well. Not only can the location of the failure surfaces vary, but the mode of failure also changes. Such information is equally valuable to engineering practitioners. In this paper, two slope examples that are modified from a real case study are presented. The first example pertains to the stability analysis of a multi-layer -slope while the second example deals with the serviceability analysis of a multi-layer c-φ slope. In addition, due to the large number of simulations needed to reveal the full picture of the failure mechanism, Convolutional Neural Networks (CNNs) that adopt a U-Net architecture is proposed to offer a soft computing strategy to facilitate the investigation. The spatial distribution of the failure surfaces, the statistics of the sliding volume, and the statistics of the deformation field are presented. The results also show that the proposed deep-learning model is effective in predicting the failure mechanism and deformation field of slopes in spatially variable soils; therefore encouraging probabilistic study of slopes in practical scenarios.

This study explores the connections between renewable energy consumption (REC), non-renewable energy consumption (NREC), gross fixed capital formation (GFCF), the labor force (LF), and economic growth (GDP) in Renewable Energy Country Attractiveness Index (RECAI) countries for 1991–2016. We quantify the nexus between REC, NREC, and GDP while utilizing a production model framework and including the measures of labor and capital, for suggesting a phase-wise strategy to attain the sustainable development goals. We use robust methodologies including Lagrange Multiplier (LM) panel unit root tests with trend shifts, Westerlund cointegration test, LM bootstrap technique for cointegration with breaks, continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) estimators, Augmented Mean Group (AMG) approach, fully modified ordinary least squares, dynamic ordinary least squares, Canonical Cointegrating Regression (CCR), and panel causality test proposed by Canning & Pedroni. We compute non-parametric time-varying coefficients with fixed effects for seeing the impact of GFCF, LF, REC, and NREC on GDP. Our results press upon policymakers to shift toward clean energy and REC for attaining the environmental goals (SDGs 6, 7, 13, and 15) and the economic goals (SDGs 1, 2, 8, and 10). While this shift would help developed economies, which have already attained the economic goals, to progress on the front of environmental goals, it would enable developing countries to progress on both fronts in a balanced manner.

Natural resource scarcity, fossil fuel energy consumption, and total greenhouse gas emissions are critical issues that require immediate attention. With the global population steadily increasing and economies expanding, the demand for natural resources, particularly fossil fuels, has experienced an unprecedented surge. This surge in consumption is directly linked to the alarming rise in greenhouse gas emissions. The study examines the nexus between agricultural nitrous oxide emissions and natural resource scarcity, taking into account the dynamics of agriculture, forestry, fishing value addition, fossil fuels, and total greenhouse gas emissions in top-emitting countries between 1971 and 2020. Natural resource scarcity positively correlates with agriculture, forestry, fishing, fossil fuel energy consumption, and total greenhouse gas emissions. There is a decrease in natural resource scarcity in countries that emit agricultural nitrous oxide, forestry, fishing emissions, fossil fuel energy consumption, and greenhouse gas emissions. Policy-makers may promote sustainable development, mitigate climate change, and ensure the long-term viability of agricultural systems by addressing the dynamics of agriculture, forestry, and fishing value addition in top-emitting countries. Through strategic policy interventions, supported by technology transfer, capacity building, and market-based instruments, the agricultural, forestry, and fishing sector can achieve a more sustainable future while addressing the challenges of natural resource scarcity.